packages = c('ggiraph', 'plotly',
'DT', 'patchwork',
'gganimate', 'tidyverse',
'readxl', 'gifski', 'gapminder',
'treemap', 'treemapify',
'rPackedBar')
for (p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p,character.only = T)
}
GDP <- read_csv("./data/Exam_data.csv")
exam_data <- read_csv("data/Exam_data.csv")
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618
)
ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot(binwidth=2.5,
dotsize = 0.5) +
scale_y_continuous(NULL,
breaks = NULL)
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618
)
exam_data$tooltip <- c(paste0(
"Name = ", exam_data$ID,
"\n Class = ", exam_data$CLASS))
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = exam_data$tooltip),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 8,
height_svg = 8*0.618
)
tooltip_css <- "background-color:white;
font-style:bold; color:black;"
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list(
opts_tooltip(
css = tooltip_css))
)
tooltip <- function(y, ymax, accuracy = .01) {
mean <- scales::number(y, accuracy = accuracy)
sem <- scales::number(ymax - y, accuracy = accuracy)
paste("Mean maths scores:", mean, "+/-", sem)
}
gg_point <- ggplot(data=exam_data,
aes(x = RACE),
) +
stat_summary(aes(y = MATHS,
tooltip = after_stat(
tooltip(y, ymax))),
fun.data = "mean_se",
geom = GeomInteractiveCol,
fill = "light blue"
) +
stat_summary(aes(y = MATHS),
fun.data = mean_se,
geom = "errorbar", width = 0.2, size = 0.2
)
girafe(ggobj = gg_point,
width_svg = 8,
height_svg = 8*0.618)
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618
)
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
)
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(tooltip = CLASS,
data_id = CLASS),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
)
exam_data$onclick <- sprintf("window.open(\"%s%s\")",
"https://www.moe.gov.sg/schoolfinder?journey=Primary%20school", as.character(exam_data$ID))
p <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(onclick = onclick),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
scale_y_continuous(NULL,
breaks = NULL)
girafe(
ggobj = p,
width_svg = 6,
height_svg = 6*0.618)
p1 <- ggplot(data=exam_data,
aes(x = MATHS)) +
geom_dotplot_interactive(
aes(data_id = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
coord_cartesian(xlim=c(0,100)) +
scale_y_continuous(NULL,
breaks = NULL)
p2 <- ggplot(data=exam_data,
aes(x = ENGLISH)) +
geom_dotplot_interactive(
aes(data_id = ID),
stackgroups = TRUE,
binwidth = 1,
method = "histodot") +
coord_cartesian(xlim=c(0,100)) +
scale_y_continuous(NULL,
breaks = NULL)
girafe(code = print(p1 / p2),
width_svg = 6,
height_svg = 6,
options = list(
opts_hover(css = "fill: #202020;"),
opts_hover_inv(css = "opacity:0.2;")
)
)
plot_ly(data = exam_data,
x = ~MATHS,
y = ~ENGLISH)
plot_ly(data = exam_data,
x = ~ENGLISH,
y = ~MATHS,
color = ~RACE)
plot_ly(data = exam_data,
x = ~ENGLISH,
y = ~MATHS,
color = ~RACE,
colors = "Set1")
pal <- c("red", "purple", "blue", "green")
plot_ly(data = exam_data,
x = ~ENGLISH,
y = ~MATHS,
color = ~RACE,
colors = pal)
plot_ly(data = exam_data,
x = ~ENGLISH,
y = ~MATHS,
text = ~paste("Student ID:", ID,
"<br>Class:", CLASS),
color = ~RACE,
colors = "Set1")
plot_ly(data = exam_data,
x = ~ENGLISH,
y = ~MATHS,
text = ~paste("Student ID:", ID,
"<br>Class:", CLASS),
color = ~RACE,
colors = "Set1") %>%
layout(title = 'English Score versus Maths Score ',
xaxis = list(range = c(0, 100)),
yaxis = list(range = c(0, 100)))
p <- ggplot(data=exam_data,
aes(x = MATHS,
y = ENGLISH)) +
geom_point(dotsize = 1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
ggplotly(p)
p1 <- ggplot(data=exam_data,
aes(x = MATHS,
y = ENGLISH)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
p2 <- ggplot(data=exam_data,
aes(x = MATHS,
y = SCIENCE)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
subplot(ggplotly(p1),
ggplotly(p2))
d <- highlight_key(exam_data)
p1 <- ggplot(data=d,
aes(x = MATHS,
y = ENGLISH)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
p2 <- ggplot(data=d,
aes(x = MATHS,
y = SCIENCE)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
subplot(ggplotly(p1),
ggplotly(p2))
DT::datatable(exam_data)
d <- highlight_key(exam_data)
p <- ggplot(d,
aes(ENGLISH,
MATHS)) +
geom_point(size=1) +
coord_cartesian(xlim=c(0,100),
ylim=c(0,100))
gg <- highlight(ggplotly(p),
"plotly_selected")
crosstalk::bscols(gg,
DT::datatable(d),
widths = 5)
globalPop <- read_xls("data/GlobalPopulation.xls")
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young')
ggplot(globalPop, aes(x = Old, y = Young,
size = Population,
colour = Country)) +
geom_point(alpha = 0.7,
show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
labs(title = 'Year: {frame_time}',
x = '% Aged',
y = '% Young') +
transition_time(Year) +
ease_aes('linear')
GDP <- read_csv("./data/GDP.csv")
WorldCountry <- read_csv("./data/WorldCountry.csv")
WorldCountry <- read_csv("./data/WorldCountry.csv")
GDP_selected <- GDP %>%
mutate(Values = as.numeric(`2020`)) %>%
select(1:3, Values) %>%
pivot_wider(names_from = `Series Name`,
values_from = `Values`) %>%
left_join(y=WorldCountry, by = c("Country Code" = "ISO-alpha3 Code"))
treemap(GDP_selected,
index=c("Continent", "Country Name"),
vSize="GDP (current US$)",
vColor="GDP (current US$)",
title="GDP (current US$) , 2020",
title.legend = "GDP per capita (current US$)"
)
treemap(GDP_selected,
index=c("Continent", "Country Name"),
vSize="GDP (current US$)",
vColor="GDP per capita (current US$)",
type = "value",
title="GDP (current US$) , 2020",
title.legend = "GDP per capita (current US$)"
)
treemap(GDP_selected,
index=c("Continent", "Country Name"),
vSize="GDP (current US$)",
vColor="GDP per capita (current US$)",
type = "value",
algorithm = "squarified",
title="GDP (current US$) , 2020",
title.legend = "GDP per capita (current US$)"
)
library(devtools)
install_github("timelyportfolio/d3treeR")
Designing Interactive Treemap using d3treeR
d3treeR package is not in Cran, you will load the devtools library and install the package found in github by using the codes below.
library(d3treeR)